Emerging highly transmissible viral infections such as SARS-CoV-2 pose a significant global threat to human health and the economy. Since its first appearance in December 2019 in the city of Wuhan, Hubei province, China, SARS-CoV-2 infection has quickly spread across the globe, with the first case reported on the African continent, in Egypt on February 14th, 2020. Although the global number of COVID-19 infections has increased exponentially since the beginning of the pandemic, the number of new infections and deaths recorded in African countries have been relatively modest, suggesting slower transmission dynamics of the virus on the continent, a lower case fatality rate, or simply a lack of testing or reliable data. Notably, there is no significant increase in unexplained pneumonias or deaths on the continent which could possibly indicate the effectiveness of interventions introduced by several African governments. However, there has not yet been a comprehensive assessment of sub-Saharan Africa’s (SSA) preparedness and response to the COVID-19 pandemic that may have contributed to prevent an uncontrolled outbreak so far. As a group of early career scientists and the next generation of African scientific leaders with experience of working in medical and diverse health research fields in both SSA and resource-rich countries, we present a unique perspective on the current public health interventions to fight COVID-19 in Africa. Our perspective is based on extensive review of the available scientific publications, official technical reports and announcements released by governmental and non-governmental health organizations as well as from our personal experiences as workers on the COVID-19 battlefield in SSA. We documented public health interventions implemented in seven SSA countries including Uganda, Kenya, Rwanda, Cameroon, Zambia, South Africa and Botswana, the existing gaps and the important components of disease control that may strengthen SSA response to future outbreaks.

Mycobacterium tuberculosis drug resistance is a threat to global tuberculosis (TB) control. Comprehensive and timely drug susceptibility determination is critical to inform appropriate treatment of drug-resistant tuberculosis (DR-TB). Phenotypic drug susceptibility testing (DST) is the gold standard for M. tuberculosis drug resistance determination. M. tuberculosis whole genome sequencing (WGS) has the potential to be a one-stop method for both comprehensive DST and epidemiological investigations. We discuss in this review the tremendous opportunities that next-generation WGS presents in terms of understanding the molecular epidemiology of tuberculosis and mechanisms of drug resistance. The potential clinical value and public health impact in the areas of DST for patient management and tracing of transmission chains for timely public health intervention are also discussed. We present the current challenges for the implementation of WGS in low and middle-income settings. WGS analysis has already been adapted routinely in laboratories to inform patient management and public health interventions in low burden high-income settings such as the United Kingdom. We predict that the technology will be adapted similarly in high burden settings where the impact on the epidemic will be greatest.

Emerging highly transmissible viral infections such as SARS-CoV-2 pose a significant global threat to human health and the economy. Since its first appearance in December 2019 in the city of Wuhan, Hubei province, China, SARS-CoV-2 infection has quickly spread across the globe, with the first case reported on the African continent, in Egypt on February 14th, 2020. Although the global number of COVID-19 infections has increased exponentially since the beginning of the pandemic, the number of new infections and deaths recorded in African countries have been relatively modest, suggesting slower transmission dynamics of the virus on the continent, a lower case fatality rate, or simply a lack of testing or reliable data. Notably, there is no significant increase in unexplained pneumonias or deaths on the continent which could possibly indicate the effectiveness of interventions introduced by several African governments. However, there has not yet been a comprehensive assessment of sub-Saharan Africa’s (SSA) preparedness and response to the COVID-19 pandemic that may have contributed to prevent an uncontrolled outbreak so far. As a group of early career scientists and the next generation of African scientific leaders with experience of working in medical and diverse health research fields in both SSA and resource-rich countries, we present a unique perspective on the current public health interventions to fight COVID-19 in Africa. Our perspective is based on extensive review of the available scientific publications, official technical reports and announcements released by governmental and non-governmental health organizations as well as from our personal experiences as workers on the COVID-19 battlefield in SSA. We documented public health interventions implemented in seven SSA countries including Uganda, Kenya, Rwanda, Cameroon, Zambia, South Africa and Botswana, the existing gaps and the important components of disease control that may strengthen SSA response to future outbreaks.

Molecular typing of Mycobacterium tuberculosis (M.tb) isolates can inform Tuberculosis (TB) control programs on the relative proportion of transmission driving the TB epidemic. There is limited data on the M. tb genotypes that are circulating in Botswana. The aim of this study was to generate baseline data on the genetic diversity of M.tb isolates circulating in the country. A total of 461 M.tb isolates received at the Botswana National Tuberculosis Reference Laboratory between March 2012 and October 2013 were included in this study. Drug susceptibility testing was conducted using the BD BACTEC MGIT 960 System. M.tb strains were genotyped using spoligotyping and spoligotype patterns were compared with existing patterns in the SITVIT Web database. A subset of drug resistant isolates which formed spoligo clusters (n = 65) was additionally genotyped with 12-loci MIRU. Factors associated with drug resistance and clustering were evaluated using logistic regression. Of the 461 isolates genotyped, 458 showed 108 distinct spoligotype patterns. The predominant M.tb lineages were Lineage 4 (81.9%), Lineage 2 (9%) and Lineage 1 (7.2%). The predominant spoligotype families within Lineage 4 were LAM (33%), S (14%), T (16%), X (16%). Three hundred and ninety-two (86%) isolates could be grouped into 44 clusters (2–46 isolates per cluster); giving a clustering rate of 76%. We identified 173 (37.8%) drug resistant isolates, 48 (10.5%) of these were multi-drug resistant. MIRU typing of the drug resistant isolates allowed grouping of 46 isolates into 14 clusters, giving a clustering rate of 49.2%. There was no association between age, sex, treatment category, region and clustering. This study highlights the complexity of the TB epidemic in Botswana with multiple strains contributing to disease and provides baseline data on the population structure of M.tb strains in Botswana.

Background: The emergence of drug-resistant Mycobacterium tuberculosis (M. tb) strains remains a threat to tuberculosis (TB) prevention and care. Understanding the drug resistance profiles of circulating strains is crucial for effective TB control. This study aimed to describe the genetic diversity of rifampicin-resistant M. tb strains circulating in Botswana using whole genome sequencing (WGS).Methods: This study included 202 stored M. tb isolates from people diagnosed with rifampicin-resistant TB (RR-TB) between January 2016 and June 2023. Genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method. Library preparation was performed using the Illumina DNA prep kit following the manufacturer's instructions. Sequencing was done on Illumina NextSeq2000. TBProfiler software was used to identify known M. tb lineages and drug resistance profiles. Statistical analyses were performed on STATA version 18.Results: WGS analysis revealed multidrug resistance (57.9%: 95% CI; 50.7–64.8), Pre-XDR (16.8%, 95% CI: 11.9–22.7), RR-TB (20.2%: 95% CI: 14.98–26.5), and HR-TB (0.5%, 95% CI; 0.01–2.7). We identified a high genetic diversity with three predominant lineages: lineage 4 (60.9%, 95% CI; 53.8–67.7), lineage 1 (22.8%: 95% CI; 17.2–29.2), and lineage 2 (13.9%, 95% CI: 9.4–19.4). The most frequently observed drug resistance mutations for rifampicin, isoniazid, ethambutol, streptomycin, pyrazinamide, and fluoroquinolones were rpoB S450L (28.6%), katG S315T (60.5%), embA_c.-29_-28delCT, embB Q497R (31.7%), rrs_n.517C>T (47.1%), pncA_c.375_389delCGATGAGGTCGATGT (36.0%) and gyrA A90V (79.4%), respectively. No bedaquiline and delamanid resistance-associated mutations were detected.Conclusions: This study highlights the high genetic diversity of M. tb strains, with a predominance of lineage 4 among people with RR-TB in Botswana. It provides valuable insights into the genetic diversity of rifampicin-resistant M. tb strains circulating in Botswana.

There is a high burden of tuberculosis (TB) in HIV antiretroviral programmes in Africa. However, few studies have looked at predictors of incident TB while on Truvada-based combination antiretroviral therapy (cART) regimens. We estimated TB incidence among individuals enrolled into an observational cohort evaluating the efficacy and tolerability of Truvada-based cART in Gaborone, Botswana between 2008 and 2011. We used Cox proportional hazards regressions to determine predictors of incident TB. Of 300 participants enrolled, 45 (15%) had a diagnosis of TB at baseline. During 428 person-years (py) of follow-up, the incidence rate of TB was 3.04/100py (95% CI, 1.69–5.06), with 60% of the cases occurring within 3 months of ART initiation. Incident cases had low baseline CD4+ T cell counts (153cells/mm³ [Q1, Q3: 82, 242]; p = 0.69) and hemoglobin levels (9.2g/dl [Q1, Q3: 8.5,10.1]; p<0.01). In univariate analysis, low BMI (HR = 0.73; 95% CI 0.58–0.91; p = 0.01) and hemoglobin levels <8 g/dl (HR = 10.84; 95%CI: 2.99–40.06; p<0.01) were risk factors for TB. Time to incident TB diagnosis was significantly reduced in patients with poor immunological recovery (p = 0.04). There was no association between baseline viral load and risk of TB (HR = 1.75; 95%CI: 0.70–4.37). Low hemoglobin levels prior to initiation of ART are significant predictors of incident tuberculosis. Therefore, there is potential utility of iron biomarkers to identify patients at risk of TB prior to initiation on ART. Furthermore, additional strategies are required for patients with poor immunological recovery to reduce excess risk of TB while on ART.